How to Sell What You Build
We won first place at the AWS x Datadog x Anthropic Hackathon. Our code wasn't the best in the room. Here's what actually won.

I'm Lang, a Forward Deployed Engineer at Galatiq. I ship AI-powered products from 0→1 — 10 apps in 10 weeks, from a winning AWS hackathon automated red-teaming platform to voice-to-PR agents.
BU Computer Engineering '25, Technology Innovation concentration. Ex-Cadence Design Systems.
Building for today's problems. Architecting for tomorrow's.

Forward Deployed Engineer
April 2026 - Present · Austin, TX

AI Engineer
July 2025 - March 2026 · San Francisco, CA

Machine Learning Engineering Intern
May 2024 - Aug 2024 · Austin, TX

GLV Team Lead
2023 - 2024 · Boston, MA

Research Assistant
Feb 2023 - July 2023 · University of Sydney

Automated red-teaming platform that discovers vulnerabilities in LLM-based agent systems. Four-stage multi-agent pipeline (Recon → Plan → Attack → Report) built on AWS Strands Agents + Bedrock probes target agents across 6 attack categories including prompt injection, secret exfiltration, and policy bypass. Features Neo4j attack surface visualization, Datadog trace deep-linking, and severity-rated vulnerability reports with actionable remediation steps.



AI-powered video ad generator that transforms any product URL into professional short-form video advertisements in under 60 seconds. Features Claude Agent SDK orchestration, dual video providers (FreePik WAN 2.6 & Kie.ai Veo 3), intelligent product metadata extraction, and a FastAPI dashboard with real-time job tracking.

AI-powered Chrome extension that reduces article reading time by 12 min on average. Full-stack product with Chrome Extension (MV3), 7 serverless API endpoints, Stripe subscriptions, and Next.js marketing site. Features parallel AI processing pipeline (4 concurrent LLM calls in 3-5s), Google OAuth, and Notion-inspired UI with dual reading modes.
We won first place at the AWS x Datadog x Anthropic Hackathon. Our code wasn't the best in the room. Here's what actually won.
The raw story of my job search — what worked, what didn't, and what I learned along the way.